Method and apparatus for controlling an industrial gas plant complex

ABSTRACT

There is provided a method of controlling an industrial gas plant complex comprising a plurality of industrial gas plants powered by one or more renewable power sources, the method being executed by at least one hardware processor, the method comprising receiving time-dependent predicted power data for a pre-determined future time period from the one or more renewable power sources; receiving time-dependent predicted operational characteristic data for each industrial gas plant; utilizing the predicted power data and predicted characteristic data in an optimization model to generate a set of state variables for the plurality of industrial gas plants; utilizing the generated state variables to generate a set of control set points for the plurality of industrial gas plants; and sending the control set points to a control system to control the industrial gas plant complex by adjusting one or more control set points of the industrial gas plants.

TECHNICAL FIELD

The present invention relates to a method and system for controlling anindustrial gas plant complex comprising a plurality of industrial gasplants powered by one or more renewable power sources.

BACKGROUND

An industrial gas plant complex may comprise one or more process plantswhich produce, or are involved in the production of, gases. Innon-limiting examples, these gases may comprise: industrial gases,commercial gases, medical gases, inorganic gases, organic gases, fuelgases and green fuel gases either in gaseous, liquified or compressedform.

There is considerable interest in methods and systems for utilisingrenewable energy sources for powering industrial gas plants andindustrial gas plant complexes. However, a significant drawback of theuse of renewable energy sources such as wind, solar and tidal power isthe natural variability and transient nature of such energy sources.

In general, a constant or substantially constant power supply ispreferred for an industrial gas plant or industrial gas plant complex.Therefore, the variable and intermittent nature of wind, solar and/ortidal power is problematic and renders it difficult to ensure maximumutilisation of an industrial gas plant or industrial gas plant complexutilizing such power sources.

An exemplary industrial gas is Ammonia. Ammonia is produced usingHydrogen from water electrolysis and nitrogen separated from the air.These gases are then fed into the Haber-Bosch process, where Hydrogenand Nitrogen are reacted together at high temperatures and pressures toproduce ammonia.

There is considerable interest in the production of Ammonia usingrenewable energy. This is known as green Ammonia. However, Ammoniasynthesis can be particularly sensitive to the variation in input energyfrom renewable sources. Thus, solutions to these technical problems arerequired to enable industrial gases to be produced efficiently in sucharrangements.

BRIEF SUMMARY OF THE INVENTION

The following introduces a selection of concepts in a simplified form inorder to provide a foundational understanding of some aspects of thepresent disclosure. The following is not an extensive overview of thedisclosure, and is not intended to identify key or critical elements ofthe disclosure or to delineate the scope of the disclosure. Thefollowing merely summarizes some of the concepts of the disclosure as aprelude to the more detailed description provided thereafter.

According to a first aspect, there is provided a method of determiningand utilizing predicted available power resources from one or morerenewable power sources for one or more industrial gas plants comprisingone or more storage resources, the method executed by at least onehardware processor, the method comprising: obtaining historicaltime-dependent environmental data associated with the one or morerenewable power sources; obtaining historical time-dependent operationalcharacteristic data associated with the one or more renewable powersources; training a machine learning model based on the historicaltime-dependent environmental data and the historical time-dependentoperational characteristic data; executing the trained machine learningmodel to predict available power resources for the one or moreindustrial gas plants for a pre-determined future time period; andcontrolling the one or more industrial gas plants in response to thepredicted available power resources for the pre-determined future timeperiod.

In embodiments, controlling the one or more industrial gas plantscomprises maximizing the usage of the predicted available powerresources for the pre-determined future time period.

In embodiments, the storage resources comprise one or more industrialgas storage vessels and/or one or more energy storage resources.

In embodiments, the one or more energy storage resources comprises oneor more of: battery energy storage systems; compressed air energystorage; liquid air energy storage; or pumped hydroelectric energystorage.

In embodiments, maximizing the usage of the predicted power resourcesfurther comprises controlling the utilization of the industrial gasstorage vessels and/or one or more energy storage resources in responseto the predicted available power resources.

In embodiments, controlling the utilization comprises utilizing analgorithm to select one or more storage resources from a group ofstorage resources for a given pattern of predicted power availability asa function of time.

In embodiments, selection of storage resources is based on physicalcharacteristics of the storage resources.

In embodiments, the one or more renewable power sources comprise one ormore of: solar power sources; wind power sources; tidal; hydro power; orgeothermal power sources.

In embodiments, the environmental data is selected from one or more of:wind speed; cloud cover; precipitation; humidity; air temperature;atmospheric pressure; solar intensity; and tide times.

In embodiments, the operational characteristic data comprises poweroutput from the one or more renewable power sources.

In embodiments, the step of training the machine learning model iscarried out periodically at a pre-determined training time.

In embodiments, at the training time the machine learning model istrained based on historical time-dependent environmental data and thehistorical time-dependent operational characteristic data obtainedwithin one or more pre-determined historical time windows.

In embodiments, the method further comprises comparing the value of thepredicted power resources for a pre-determined future time period withthe actual power resources at the end of the predicted period togenerate a prediction error value.

In embodiments, the pre-determined training time is selected when theprediction error value exceeds a pre-determined threshold.

In embodiments, the pre-determined training time is selected based on apre-determined empirical interval unless the prediction error valueexceeds the pre-determined threshold within the pre-determined empiricalinterval.

In embodiments, the one or more industrial gas plants comprise aHydrogen production plant comprising at least one electrolyzer.

In embodiments, the one or more industrial gas plants comprise anAmmonia production plant complex including the Hydrogen productionplant.

In embodiments, the machine learning model comprises one or more of:Gradient boosting algorithm; Long short-term memory (LSTM) algorithm;support vector machine (SVM) algorithm; or random decision forestalgorithm.

According to a second aspect, there is provided a system for determiningand utilizing predicted available power resources from one or morerenewable power sources for one or more industrial gas plants comprisingone or more storage resources, the system comprising: at least onehardware processor operable to perform: obtaining historicaltime-dependent environmental data associated with the one or morerenewable power sources; obtaining historical time-dependent operationalcharacteristic data associated with the one or more renewable powersources; training a machine learning model based on the historicaltime-dependent environmental data and the historical time-dependentoperational characteristic data; executing the trained machine learningmodel to predict available power resources for the one or moreindustrial gas plants for a pre-determined future time period; andcontrolling the one or more industrial gas plants in response to thepredicted available power resources for the pre-determined future timeperiod.

According to a third aspect, there is provided a computer readablestorage medium storing a program of instructions executable by a machineto perform a method of determining and utilizing predicted availablepower resources from one or more renewable power sources for one or moreindustrial gas plants comprising one or more storage resources, themethod comprising: obtaining historical time-dependent environmentaldata associated with the one or more renewable power sources; obtaininghistorical time-dependent operational characteristic data associatedwith the one or more renewable power sources; training a machinelearning model based on the historical time-dependent environmental dataand the historical time-dependent operational characteristic data;executing the trained machine learning model to predict available powerresources for the industrial gas plant for a pre-determined future timeperiod; and controlling the one or more industrial gas plants inresponse to the predicted available power resources for thepre-determined future time period.

According to a fourth aspect, there is provided a method of monitoringoperational characteristics of an industrial gas plant complexcomprising a plurality of industrial gas plants, the method beingexecuted by at least one hardware processor, the method comprising:assigning a machine learning model to each of the industrial gas plantsforming the industrial gas plant complex; training the respectivemachine learning model for each industrial gas plant based on receivedhistorical time-dependent operational characteristic data for therespective industrial gas plant; executing the trained machine learningmodel for each industrial gas plant to predict operationalcharacteristics for each respective industrial gas plant for apre-determined future time period; and comparing predicted operationalcharacteristic data for each respective industrial gas plant for apre-determined future time period with measured operationalcharacteristic data for the corresponding time period to identifydeviations in industrial gas plant performance.

In embodiments, the step of comparing is carried out at the end of thepre-determined future time period of the predicted operationalcharacteristic data or at a timestamp therein.

In embodiments, the step of comparing comprises comparing predictedoperational characteristic data predicted for a pre-determined timewindow with actual measured operational characteristic data for the sametime window.

In embodiments, the received historical time-dependent operationalcharacteristic data for the respective industrial gas plant comprisesdata obtained from a direct measurement of a process or parameter of therespective industrial gas plant.

In embodiments, the received historical time-dependent operationalcharacteristic data for the respective industrial gas plant comprisesdata obtained from a physics-based model representative of operationalcharacteristics of the respective industrial gas plant.

In embodiments, measured data relating to a process or parameter of therespective industrial gas plant is input into the respectivephysics-based model.

In embodiments, the predicted operational characteristics for eachindustrial gas plant are utilized to determine predicted futureresources, future failure and/or predicted future maintenance.

In embodiments, one or more of the industrial gas plants comprises ahydrogen process plant having a plurality of electrolyzer modules.

In embodiments, each of the electrolyzer modules is assigned a machinelearning model.

In embodiments, the predicted operational characteristics for eachrespective industrial gas plant are utilized in a further model togenerate an operational performance metric of the industrial gas plantcomplex.

In embodiments, the operational performance metric comprises anefficiency value for the industrial gas plant complex.

In embodiments, the industrial gas plant complex comprises an Ammoniaplant complex and the determined efficiency value enables a predicteddetermination of the Ammonia produced for a given level of energy input.

According to a fifth aspect, there is provided a system for monitoringoperational characteristics of an industrial gas plant complexcomprising a plurality of industrial gas plants, the system comprisingat least one hardware processor operable to perform assigning a machinelearning model to each of the industrial gas plants forming theindustrial gas plant complex; training the respective machine learningmodel for each industrial gas plant based on received historicaltime-dependent operational characteristic data for the respectiveindustrial gas plant; executing the trained machine learning model foreach industrial gas plant to predict operational characteristics foreach respective industrial gas plant for a pre-determined future timeperiod; and comparing predicted operational characteristic data for eachrespective industrial gas plant for a pre-determined future time periodwith measured operational characteristic data for the corresponding timeperiod to identify deviations in industrial gas plant performance.

In embodiments, the step of comparing is carried out at the end of thepre-determined future time period of the predicted operationalcharacteristic data or at a timestamp therein.

In embodiments, the step of comparing comprises comparing predictedoperational characteristic data predicted for a pre-determined timewindow with actual measured operational characteristic data for the sametime window.

In embodiments, the received historical time-dependent operationalcharacteristic data for the respective industrial gas plant comprisesdata obtained from a direct measurement of a process or parameter of therespective industrial gas plant.

In embodiments, the received historical time-dependent operationalcharacteristic data for the respective industrial gas plant comprisesdata obtained from a physics-based model representative of operationalcharacteristics of the respective industrial gas plant.

In embodiments, the predicted operational characteristics for eachindustrial gas plant are utilized to determine predicted futureresources, future failure and/or predicted future maintenance.

In embodiments, the predicted operational characteristics for eachrespective industrial gas plant are utilized in a further model togenerate an operational performance metric of the industrial gas plantcomplex.

According to a sixth aspect, there is provided a computer readablestorage medium storing a program of instructions executable by a machineto perform a method of monitoring operational characteristics of anindustrial gas plant complex comprising a plurality of industrial gasplants, the method being executed by at least one hardware processor,the method comprising: assigning a machine learning model to each of theindustrial gas plants forming the industrial gas plant complex; trainingthe respective machine learning model for each industrial gas plantbased on received historical time-dependent operational characteristicdata for the respective industrial gas plant; executing the trainedmachine learning model for each industrial gas plant to predictoperational characteristics for each respective industrial gas plant fora pre-determined future time period; and comparing predicted operationalcharacteristic data for each respective industrial gas plant for apre-determined future time period with measured operationalcharacteristic data for the corresponding time period to identifydeviations in industrial gas plant performance.

According to a seventh aspect, there is provided a method of controllingan industrial gas plant complex comprising a plurality of industrial gasplants powered by one or more renewable power sources, the method beingexecuted by at least one hardware processor, the method comprising:receiving time-dependent predicted power data for a pre-determinedfuture time period from the one or more renewable power sources;receiving time-dependent predicted operational characteristic data foreach industrial gas plant; utilizing the predicted power data andpredicted characteristic data in an optimization model to generate a setof state variables for the plurality of industrial gas plants; utilizingthe generated state variables to generate a set of control set pointsfor the plurality of industrial gas plants; and sending the control setpoints to a control system to control the industrial gas plant complexby adjusting one or more control set points of the industrial gasplants.

In embodiments, the optimization model defines the predicted power dataand predicted characteristic data as a set of non-linear equations.

In embodiments, the state variables are generated by solving the set ofnon-linear equations.

In embodiments, the time-dependent predicted power data is generatedfrom a trained machine learning model.

In embodiments, the time-dependent predicted power data is obtained by:obtaining historical time-dependent environmental data associated withthe one or more renewable power sources; obtaining historicaltime-dependent operational characteristic data associated with the oneor more renewable power sources; training a machine learning model basedon the historical time-dependent environmental data and the historicaltime-dependent operational characteristic data; and executing thetrained machine learning model to predict available power resources forthe one or more industrial gas plants for a pre-determined future timeperiod.

In embodiments, the time-dependent predicted operational characteristicdata is generated from a trained machine learning model for each of theindustrial gas plants.

In embodiments, the time-dependent predicted operational characteristicdata for each industrial plant is obtained by: assigning a machinelearning model to each of the industrial gas plants forming theindustrial gas plant complex; training the respective machine learningmodel for each industrial gas plant based on received historicaltime-dependent operational characteristic data for the respectiveindustrial gas plant; and executing the trained machine learning modelfor each industrial gas plant to predict operational characteristics foreach respective industrial gas plant for a pre-determined future timeperiod.

In embodiments, the industrial gas plant complex comprises storageresources comprising one or more industrial gas storage vessels and/orone or more energy storage resources.

In embodiments, the one or more energy storage resources comprises oneor more of: battery energy storage systems; compressed air energystorage; liquid air energy storage; or pumped hydroelectric energystorage.

In embodiments, the predicted power data further comprises datarepresentative of operational parameters of the storage resources.

In embodiments, the data representative of operational parameters of thestorage resources comprises one or more of: resource storageavailability; fill level; and utilization.

According to an eighth aspect, there is provided a system forcontrolling an industrial gas plant complex comprising a plurality ofindustrial gas plants powered by one or more renewable power sources,the system comprising: at least one hardware processor operable toperform: receiving time-dependent predicted power data for apre-determined future time period from the one or more renewable powersources; receiving time-dependent predicted operational characteristicdata for each industrial gas plant; utilizing the predicted power dataand predicted characteristic data in an optimization model to generate aset of state variables for the plurality of industrial gas plants;utilizing the generated state variables to generate a set of control setpoints for the plurality of industrial gas plants; and sending thecontrol set points to a control system to control the industrial gasplant complex by adjusting one or more control set points of theindustrial gas plants.

In embodiments, the optimization model defines the predicted power dataand predicted characteristic data as a set of non-linear equations.

In embodiments, the state variables are generated by solving the set ofnon-linear equations.

In embodiments, the time-dependent predicted power data is generatedfrom a trained machine learning model.

In embodiments, the time-dependent predicted operational characteristicdata is generated from a trained machine learning model for each of theindustrial gas plants.

According to a ninth aspect, there is provided a computer readablestorage medium storing a program of instructions executable by a machineto perform a of controlling an industrial gas plant complex comprising aplurality of industrial gas plants powered by one or more renewablepower sources, the method being executed by at least one hardwareprocessor, the method comprising: receiving time-dependent predictedpower data for a pre-determined future time period from the one or morerenewable power sources; receiving time-dependent predicted operationalcharacteristic data for each industrial gas plant; utilizing thepredicted power data and predicted characteristic data in anoptimization model to generate a set of state variables for theplurality of industrial gas plants; utilizing the generated statevariables to generate a set of control set points for the plurality ofindustrial gas plants; and sending the control set points to a controlsystem to control the industrial gas plant complex by adjusting one ormore control set points of the industrial gas plants.

In embodiments, the optimization model defines the predicted power dataand predicted characteristic data as a set of non-linear equations.

In embodiments, the state variables are generated by solving the set ofnon-linear equations.

In embodiments, the time-dependent predicted power data is generatedfrom a trained machine learning model and/or the time-dependentpredicted operational characteristic data is generated from a trainedmachine learning model for each of the industrial gas plants.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will now be described by exampleonly and with reference to the figures in which:

FIG. 1 is a schematic diagram of an industrial gas plant complex andcontrol system;

FIG. 2 is a detailed schematic diagram of the control system of FIG. 1;

FIG. 3 is a graph showing measured and predicted wind power values usinga model according to an embodiment of the present invention;

FIG. 4 is a graph showing measured and predicted solar power valuesusing a model according to an embodiment of the present invention;

FIG. 5 is a flow chart of a method according to an embodiment;

FIG. 6 is a flow chart of a method according to an embodiment;

FIG. 7 is a graph showing the efficiency model for a process variable;

FIG. 8 is a graph of optimal Ammonia plant rate as a function of timefor a 48 hour predicted period; and

FIG. 9 is a flow chart of a method according to an embodiment.

Embodiments of the present disclosure and their advantages are bestunderstood by referring to the detailed description that follows. Itshould be appreciated that like reference numbers are used to identifylike elements illustrated in one or more of the figures, whereinshowings therein are for purposes of illustrating embodiments of thepresent disclosure and not for purposes of limiting the same.

DETAILED DESCRIPTION

Various examples and embodiments of the present disclosure will now bedescribed. The following description provides specific details for athorough understanding and enabling description of these examples. Oneof ordinary skill in the relevant art will understand, however, that oneor more embodiments described herein may be practiced without many ofthese details. Likewise, one skilled in the relevant art will alsounderstand that one or more embodiments of the present disclosure caninclude other features and/or functions not described in detail herein.Additionally, some well-known structures or functions may not be shownor described in detail below, so as to avoid unnecessarily obscuring therelevant description.

FIG. 1 shows a schematic diagram of an industrial gas plant complex 10and a control system 100.

The industrial gas plant complex 10 comprises a Hydrogen productionplant 12, a Hydrogen storage unit 14, an Air Separation Unit (ASU) 16,an Ammonia synthesis plant 18 and an ammonia storage unit 20. Theammonia storage unit 20 is connected to an external supply chain 22 foronward distribution of Ammonia.

Electricity for powering the industrial gas plant complex 10 isgenerated at least in part by renewable energy sources such as wind 24and/or the solar 26 although other sources such as a diesel-, petrol- orhydrogen-powered generator (not shown) or a national power grid (notshown) may optionally be utilised.

To address the intermittency of power supply from renewable sources, astorage resource 28 is provided. The storage resource 28 may compriseone or more resource storage devices or energy storage devices. Forexample, the one or more resource storage devices may include theHydrogen storage unit 14. Hydrogen production through electrolysisrequires a significant amount of power and the use of stored Hydrogen asa Hydrogen source for Ammonia production may significantly reduce thepower consumption of the plant 10 during periods of low renewable powersupply. Additionally, liquid Nitrogen storage 16 a may also be providedas part of the storage resource 28 as shown in FIG. 1.

Additionally or alternatively, in non-exhaustive arrangements, theenergy storage devices may comprise one or more of: a Battery EnergyStorage System (BESS) 28 a, a Compressed/Liquid Air Energy Systems (CAESor LAES) 28 b or a Pumped Hydro Storage System (PHSS) 28 c.

A BESS 28 a utilises electrochemical techniques and may comprise one ormore of: Lithium Ion batteries, Lead acid batteries, Zinc Bromine,Sodium Sulphur or Redox Flow batteries. Electro-chemical arrangementssuch as batteries have advantages in terms of fast charging rates andfast (virtually instantaneous) ramp rates to supply power to cope with asudden drop in energy supply. However, they tend to be of more limitedpower capacity than other systems. Therefore, they may be better suitedfor use in situations where, for example, a power shortfall fromrenewable sources is expected to be temporary or short in duration.

A CAES 28 b compresses air and stores the air under a high pressure ofaround 70 bar. It is usually stored in an underground cavern. When poweris required, the compressed air is heated and expanded in an expansionturbine in order to drive a generator.

A LAES 28 b comprises an air liquefier to draw air from the environmentand compress and cool the air to achieve liquefaction. The liquified airis then stored in an insulated tank until power is required. To convertthe liquified air into useable energy, the liquid air is pumped to highpressure and heated through heat exchangers. The resulting high-pressuregas is used to drive a turbine to generate electricity.

CAES and LAES are capable of storing significantly more energy than mostBESS 28 a systems. However, CAES and LAES have slower ramp rates thanelectro-chemical storage devices and require longer to store largerquantities of energy. For example, it may take of the order of 5-10minutes for a compression stage to operate under full load, and 10-20minutes to generate full power on demand. Such storage devices aretherefore more appropriate for longer-term storage and for supplyingpower during long periods of renewable energy shortfall.

A PHSS 28 c stores energy in the form of gravitational potential energyby pumping water from a lower elevation reservoir to a higher elevationreservoir. When power is required, the water is released to driveturbines. Some PHSS arrangements utilised a reversible pump-turbineunit.

Given the large storage capacity of PHSS configurations, they are oftensuited to longer-term storage. In addition, particularly for reversiblepump turbines, timescales of the order of 5-10 minutes from shutdown tofull load generation, 5 to 30 minutes from shut down to pumping, and 10to 40 minutes for pumping to load generation or vice vera are common.Thus, such storage would appear more appropriate for longer term powerdeficits.

Whilst all these elements are shown in FIG. 1, this is for illustrativepurposes only. The energy storage resource 20 need not comprise each andevery described element and may comprise only one or more of thedescribed elements. In addition, the energy resource 28 may compriseadditional elements.

The components of the industrial gas plant complex 10 will now bedescribed in detail.

Hydrogen Production Plant 12

The Hydrogen production plant 12 is operable to electrolyse water toform Hydrogen and Oxygen. Any suitable source of water may be used.However, in embodiments in which sea water is used to produce the waterfor the electrolysis, the apparatus would further comprise at least onedesalination and demineralisation plant to for processing the sea water.

The Hydrogen production plant 12 comprises a plurality of electrolysisunits 12 a, 12 b . . . 12 n or electrolysis cells. Each unit or cell maybe referred to as an “electrolyser” 12 a, 12 b . . . 12 n.

The electrolysers may enable the Hydrogen production plant 12 to have atotal capacity of at least 1 GW. However, the ultimate capacity of theHydrogen production plant 12 is limited only by practical considerationssuch as power supply.

Any suitable type of electrolyser may be used. In embodiments, theplurality of electrolysers usually consists of a multiplicity ofindividual cells combined into “modules” that also include processequipment such as pumps, coolers, and/or separators. Hundreds of cellsmay be used and may be grouped in separate buildings. Each moduletypically has a maximum capacity greater than 10 MW, although this isnot intended to be limiting.

Any suitable type of electrolyser may be used with the presentinvention. Generally, three conventional types of electrolyser areutilized—alkaline electrolysers; PEM electrolysers; and solid oxideelectrolysers. Any of these types may be used with the presentinvention.

Alkaline electrolysers transport hydroxide ions (OH⁻) through theelectrolyte from the cathode to the anode with hydrogen being generatedon the cathode side. Commonly, a liquid alkaline solution of sodiumhydroxide or potassium hydroxide is used as the electrolyte.

A PEM electrolyser utilizes a solid plastics material as an electrolyte,and water reacts at an anode to form oxygen and positively chargedhydrogen ions. The electrons flow through an external circuit and thehydrogen ions selectively move across the PEM to the cathode. At thecathode, hydrogen ions combine with electrons from the external circuitto form hydrogen gas.

Solid oxide electrolysers use a solid ceramic material as theelectrolyte that selectively conducts negatively charged oxygen ions(O²⁻) at elevated temperatures. Water at the cathode combines withelectrons from the external circuit to form hydrogen gas and negativelycharged oxygen ions. The oxygen ions pass through the solid ceramicmembrane and react at the anode to form oxygen gas and generateelectrons for the external circuit.

The electrolysers may be arranged in any suitable group. For example,they may be arranged in parallel.

Hydrogen is produced at about atmospheric pressure by the Hydrogenproduction plant 12. A stream of hydrogen so generated is removed fromthe electrolysers at a slightly elevated pressure and may be transferredvia a pipe to the Ammonia synthesis plant 18.

Alternatively, any Hydrogen surplus to requirements may be stored in theHydrogen storage unit 14. The storage unit 14 comprises of a pluralityof short-term and longer-term storage options with different sizes,filling/discharge rates, and roundtrip efficiencies. Typical storagesystem could include pressure vessels and/or pipe segments connected toa common inlet/outlet header. The pressure vessels may be spheres, forexample, to about 25 m in diameter, or “bullets” which are horizontalvessels with large L/D ratios (typically up to about 12:1) withdiameters up to about 12 m. In certain geographies, underground cavernsare included as storage systems to flatten out the seasonal variationsassociated with the renewable power.

Preferably, the Hydrogen gas is compressed by a compressor and stored inthe Hydrogen storage unit 14 under pressure to reduce volumerequirements. It may be used commercially at this point (e.g. sold forautomotive purposes) or may be used as a reservoir for Ammonia synthesisplant 18 via pipe 30.

Optionally, a purification system may be implemented to purify or drythe Hydrogen before onward use. For example, the Hydrogen may be driedin an adsorption unit, such as a temperature swing adsorption (TSA) unitfor the downstream process(es).

Air Separation Unit 16

In non-limiting embodiments, the Nitrogen gas required for Ammoniaproduction is produced by cryogenic distillation of air in the airseparation unit (ASU) 16. Typically an ASU 16 operates at a pressure ofaround 10 bar. The pressure is then reduced to provide a stream ofNitrogen gas in one or more pipes arranged to transport Nitrogen to theAmmonia Synthesis plant 16. However, other Nitrogen sources may be usedif required, for example, Nitrogen storage 16 a.

A Nitrogen gas storage unit 16 a may also be provided, which can be usedas a resource storage as described below. The storage unit 16 a may, incommon with the Hydrogen storage unit 14, comprise a plurality ofshort-term and longer-term storage options having different sizes,filling/discharge rates, and roundtrip efficiencies.

A typical storage system for Nitrogen may comprise a plurality ofpressure vessels and/or pipe segments connected to a common inlet/outletheader. The pressure vessels may be spheres, for example, to about 25 min diameter, or “bullets” which are horizontal vessels with large L/Dratios (typically up to about 12:1) with diameters up to about 12 m. Incertain geographies, underground caverns may be utilised to flatten outthe seasonal variations associated with the renewable power.

Preferably, the Nitrogen gas is compressed by a compressor and stored inthe Nitrogen storage unit 16 a under pressure to reduce volumerequirements. It may be used as a reservoir for Ammonia synthesis plant18 which may be fed by a connecting pipe.

Ammonia Synthesis Plant 18

The Ammonia Synthesis plant 18 operates on the Haber-Bosch process andcomprises an Ammonia Loop. An Ammonia Loop is a single unit equilibriumreactive system which processes the synthesis gas of Nitrogen andHydrogen to produce Ammonia.

Nitrogen is provided by one or more pipes from the ASU 16 which, inembodiments, may run continuously to provide Nitrogen. Hydrogen isprovided from one or more pipe from Hydrogen production plant 12 (if itis running based on the availability of the renewable power at giveninstance) otherwise Hydrogen is fed from the Hydrogen storage 14.

Stoichiometric composition of synthesis gas is processed by a syn-gascompressor system and the resulting Ammonia product is refrigerated byanother set of compressors and sent to storage. The performance ofAmmonia loop is governed by the equilibrium conversion of the exothermicreaction. The parameters for this will be discussed below.

Electricity Generation and Management System

Electricity for the plant 10 as a whole may be generated from anysuitable energy source, including renewable or non-renewable energysources. As shown in FIG. 1, the electricity is generated from at leastone renewable energy source of either wind energy 24 (via a suitablewind farm comprising a plurality of wind turbines) and/or solar energy26 (via a solar farm comprising a plurality of solar cells). Inaddition, other renewable energy sources may be used such ashydro-electric (not shown) and/or tidal power (not shown).

In addition, electricity or resources for the plant 10 as a whole or forsub-plants of the plant 10 may be drawn from the energy storage resource28. As described with respect to FIG. 1, the energy storage resource 28may comprise one or more storage resources. For example, the one or morestorage resources may include the Hydrogen storage unit 14 and Nitrogenstorage 16 a.

Additionally or alternatively, in non-exhaustive arrangements, theenergy storage devices may comprise one or more of: a Battery EnergyStorage System (BESS) 28 a, a Compressed/Liquid Air Energy Systems (CAESor LAES) 28 b or a Pumped Hydro Storage System (PHSS) 28 c.

These elements are used optimally to store additional resources and/orenergy when electricity provision from renewable sources is high orpredicted to be high and then utilise those resources and/or energy whenrenewable electricity resources are predicted to be low.

The prediction and control of these facilities will be described below.Selection of these facilities under optimal conditions is important suchthat the correct energy source is selected for a particular predictedpower shortfall period, for example.

Control System 100

The control system 100 of FIG. 1 is shown in detail in the schematic ofFIG. 2.

The control system 100 comprises three main categories: plant complexcontrol systems 110, renewable energy control systems 120 and anoptimization system 150. These are non-limiting terms and do notnecessarily imply any interconnection or grouping between the componentparts of the systems 110, 120, 150 and are illustrated in a commongrouping for clarity purposes only.

The plant complex control systems 110 comprise a Hydrogen productionplant control system 112, a Hydrogen storage control system 114, an ASUcontrol system 116 and an Ammonia synthesis plant control system 118.

By way of example, the Hydrogen production plant control system 112 maybe configured to monitor the amount and rate of generation of Hydrogengas from the electrolysis by measurement. Such a measurement may bederived from sensor measurements such as direct flow measurements, oralternatively inferred through indirect measurements such as theelectrolyser current or power demand.

By way of further example, for the Hydrogen storage control system 114,the pressure and flow of compressed hydrogen from electrolyser andcompression system to the storage system may be monitored, as well asthe pressure and flow of compressed hydrogen gas to the Ammoniasynthesis plant 18.

In each case the control systems 110 are operable to control theparameters of the respective industrial gas plant and are able to outputuse and process data from each industrial gas plant. This will bedescribed in detail below.

The renewable energy control systems 120 in the described embodimentscomprise the wind control system 124 and solar control system 126. Thesecontrol systems control and monitor process parameters of the renewableenergy source such as energy generation, storage and load. They are alsoconfigured to send usage, power and process data to external systems asrequired. If hydro-electric or tidal renewable power sources are used,similar control systems will apply.

The optimization system 150 comprises a computer system including threemodules: a power prediction module 152, a plant operation module 154 anda real-time optimization module 156.

The power prediction module (PPM) 152 receives usage and powergeneration data from the renewable energy control systems 120 and alsofrom a weather and forecast database 160 which comprises informationrelating to past (known and historical) environmental and weather dataand future (predicted and forecast) environmental and weather data. Thepower prediction module 152 comprises a machine learning algorithmimplemented on a computing system as will be described below and is usedto generate a model relating to future power generation.

The plant operation module (POM) 154 is operable to receive plantoperation data from the plant complex control systems 110 and generate amodel of the plant operation. The plant operation module 152 comprises amachine learning algorithm implemented on a computing system as will bedescribed below.

The real-time optimization module (RTOM) 156 is arranged to receiveinputs from the power prediction module 152 and plant operation module154 and derive a plant operation policy strategy including setpointoperation parameters. These are then fed to the plant complex controlsystems 110 to control the relevant processes controlled thereby.

The detail and operation of each component will now be described.

Power Prediction Module (Ppm) 152

The power prediction module 152 comprises a machine learning algorithmimplemented on a computing system and operable to generate a model topredict future power generation. In embodiments, an aspect of the powerprediction module 152 is to be able to predict future power generationfrom a variable and/or intermittent source such as a renewable powersource so that one or more industrial gas plants (which in generalrequire a constant power load) can be controlled without risk of powerstarvation of the plants.

In embodiments, a further aspect of the power prediction module 152 isto use the predicted power generation data to control the plant complex10 or aspects thereof. This will be discussed in more detail below.

The model used to predict future power is based on a machine learningframework. Any suitable machine learning algorithm may be used. Forexample, the model may utilise techniques such as Gradient boosting(utilising, for example, XGboost), Long short-term memory (LSTM),support vector machine (SVM) or random decision forests may be used insuch a model.

Gradient boosting is a machine learning technique utilized in regressionand classification problems. A strong prediction model is formed whichcomprises an ensemble of weak prediction models such as decision trees.A stage-wise process may be used to generate the model through steepestdescent minimisation (amongst others).

LSTM is an artificial recurrent neural network architecture which hasfeedback connections as well as feedforward connections. A common LSTMunit is composed of a cell, an input gate, an output gate and a forgetgate. The cell is operable to remember values over an arbitrary timeinterval the flow of information into and out of the cell is regulatedby the gates.

A support vector machine utilises a set of training examples, eachcomprised in one of two categories, and generates a model that assignsnew examples to a particular category. Thus, a SVM comprises anon-probabilistic binary linear classifier.

Random decision forests comprise ensemble machine learning methods whichoperate by constructing a multitude of decision trees during a trainingprocess and outputting the class that is the mode of the classes(classification) or mean/average prediction (regression) of theindividual trees.

Irrespective of the machine learning algorithm used, the model utilisesa two step operation process where a training stage is required prior toa predictive stage. Both stages are implemented as computer programs onone or more computer systems.

Specialist hardware may also be used. For example, the training stagemay involve use of Central Processing unit (CPU) and GraphicalProcessing Unit (GPU) components of a computer system. In addition,other specialist hardware may be used such as Field Programmable GateArrays (FPGAs), Application Specific Integrated Circuits (ASICs) orother stream processor technologies.

Training Stage

The model is trained periodically (for example, on a daily basis) or ondemand (if, for example, the accuracy of the model demands a trainingprocess) to create a relationship between predicted variables of therenewable energy power plant to determine predicted energy availabilityfor a pre-determined future period.

The PPM 152 aims to determine the predicted variables of:

Wind power WPi

Solar Power SPi

where the index i represents time from period n to n+k in intervals offixed duration. In non-limiting embodiments, the intervals may comprise15 minutes or 1 hour.

The predictor variables comprise time-dependent operationalcharacteristic data of the are:

Wind power WPi

Solar Power SPi

Power load Li from the period n−m to n−1.

The predictor variables also include measured environmental andmeteorological signals which may comprise but are not limited totime-dependent environmental data comprising:

Air temperature Ti,

Atmospheric pressure Pi

Wind speed WSi,

Cloud cover CCi,

Precipitation Pi

Humidity Hi

where index i represents time from period n-m to n+k.

In the training stage, various machine learning algorithms are used tocreate a mathematical relationship between the predicted and predictorvariables. These relationships are stored in the computer as a series ofequations that can be accessed and used to make future predictions.

These models are generated during a training process which is carriedout periodically at a pre-determined training time. At each trainingtime the machine learning model is trained based on the historicaltime-dependent environmental data and the historical time-dependentoperational characteristic data obtained within one or morepre-determined historical time windows. In other words, a movinghistorical window of time-dependent data (e.g. the previous 2 days, theprevious 2-weeks etc.) is used to train the machine learning model.

As noted above, meteorological measurements come from the weather andforecast database 160 which may comprise a weather data service or otherinternet-connected resource. Load, solar power and wind power aremeasured by the operator or fed via the renewable energy control systems120 as part of an automated data collection system.

Prediction Stage

In the prediction stage, the equations generated and stored in thetraining phase are used to enable predictions regarding future behaviourto be made.

The predicted variable comprises a forecast of wind power WPi and solarpower SPi for i from c+1 to c+p, where c represents current time and prepresents a prediction horizon which could be any suitable timescale;for example, non-limiting examples may be 24 hours, 48 hours or more.

The predictor variables are:

Wind power WPi

Solar Power SPi

Power load Li

from c-r to c, measured meteorological signals (as per the trainingphase), and forecast meteorological signals from c+1 to c+p.

As for the training phase, environmental and meteorological measurementscome from, in embodiments, the weather and forecast database 160 whichmay comprise a weather data service or other internet-connectedresource. Load, solar power and wind power are measured by the operatoror fed via the renewable energy control systems 120 automatically.

FIGS. 3 and 4 show prediction of wind power and solar power where thepredicted variables are generated from the model prediction stage above.

In embodiments, an assessment of the accuracy of the machine learningmodel is made by comparing a value of the predicted power resources fora pre-determined future time period with the actual power resourceswhich were available to the industrial gas plant at the end of thepredicted future time period. This enables determination of a predictionerror value which provides a metric for the accuracy of the model.

If the model is insufficiently accurate, it may need to be trained at afurther training time. The pre-determined training time may be selectedbased on the prediction error value which may, in embodiments, be whenthe prediction error value exceeds a pre-determined threshold.

In embodiments, the training time may be selected empirically on aperiodic basis; for example, every 24 hours or every 48 hours asappropriate. In embodiments, this periodic basis may be the defaulttraining time strategy.

However, in embodiments, this may be interrupted when the predictionerror value exceeds the pre-determined threshold within thepre-determined empirical interval, in which case the training time isscheduled based on the prediction error value.

Control Based on Predicted Data

The PPM 152 may utilise the predicted data to control the one or moreindustrial gas plants in response to the predicted available powerresources for the pre-determined future time period. For example, inaddition to the RTM 156, the PPM 152 may control operational aspects ofthe of the one or more industrial gas plants.

In embodiments, the forecast power data may additionally oralternatively control the use of the storage resource 28. As describedabove, the storage resource 28 may comprise resource storage such asstored Hydrogen and/or Nitrogen, and energy storage through BESS 28 a,CAES/LAES 28 b and PHSS 28 c amongst others.

The PPM 152 may therefore comprise a control and optimization algorithmwhich utilizes the predicted power resources to optimize usage of theavailable storage resources 28 to run the plant complex 10 optimally andto plan for future power availability and power usages.

The PPM 152 system uses the predicted powers WPi and SPi as inputs in areal-time optimization problem and applies an optimization algorithm topropose optimal rates at which to run the industrial gas complex 10 tomaximize the utilisation of the available power and the stored resourcesin the resource storage 28.

For example, expected daily and seasonal variability in predicted powergeneration may be addressed by the PPM 152 by determining an optimal useof short-term (e.g. BESS 28 a) and longer-term (e.g. CAES/LAES 28 b andPHSS 28 c) storage systems in the storage resource 28.

In embodiments, the PPM 152 may determine optimal power up and shut downtimes for storage resources 28 to maximize available power utilization.Examples of this will be described below.

In a non-limiting example, the PPM 152 may determine that sufficientpower is available over the next 24 hours to run the electrolyzers 12 a. . . n of the Hydrogen production plant 12 under greater load togenerate more Hydrogen than required to produce an optimal Ammoniaproduction rate over the predetermined time period. The additionalHydrogen may then be stored in the Hydrogen storage 14 for use duringlower power availability periods. The same may apply to the ASU 16 andNitrogen storage 16 a.

The PPM 152 may receive operational characteristic data for the Hydrogenstorage 14 and Nitrogen storage 16 a. This may include fill levels andother operational data (e.g. fill pressures, fill volume, density etc.)This operational data may be used by an algorithm forming part of thePPM 152 to determine optimal storage requirements to address predictedfuture power availability distribution as a function of time.

In embodiments, the PPM 152 may also determine that sufficient power isavailable over a predicted time window to store additional power in theenergy storage 28 a, 28 b, 28 c. The PPM 152 may utilize an optimizationalgorithm to select the appropriate energy storage 28 a, 28 b, 28 cdepending upon the power availability vs time predictions.

In a non-limiting example, the PPM 152 may determine that poweravailability in excess of plant complex 10 demand is available for arelatively short period (e.g. 1-2 hours). It may then be determined thatBESS 28 a represents the most appropriate energy storage solution forthat time period given the short ramp rate, fast charging time and lowercapacity of BESS 28 a solutions.

In an alternative non-limiting example, the PPM 152 may determine thatsignificant power availability in excess of plant complex 10 demand isavailable for a longer period (e.g. 5-10 hours). In this situation, itmay then be determined that CAES/LAES 28 b and PHSS 28 c may be bettersuited to storage of the available power given the slower ramp rates andhigher storage capacity of such energy storage solutions.

The PPM 152 system solves the optimization problem for the next p timeperiods applies the available power predictions in a real timeoptimization model to maximize the utilization of power. The model mayinvolve the generation of a power utilization metric and theoptimization seeks to optimize the value of the power generation metric.

The PPM 152 may also comprise a tracking system to calculate thepredicted power utilization metric as a function of complementarity ofrenewable power resources and adjust the generations levels/utilizationto maximize long term utilization of renewable resources.

The PPM 152 system is implemented on a computer and receives variousinputs from other computer systems. An example of the PPM 152 system mayutilise Mixed Integer Non Linear Programs (MINLP) because some of thedecisions require some equipment to be run in one of multiple possiblemodes leading to integer variables. FIG. 5 shows a method according toan embodiment. Note the following steps need not be carried out in theorder described below and some steps may be carried out concurrentlywith other steps.

In embodiments, a method of predicting available power resources fromone or more renewable power sources for one or more industrial gasplants is provided. The method executed by at least one hardwareprocessor.

At step 200, historical time-dependent environmental data associatedwith the one or more renewable power sources 24, 26 is obtained. Byhistorical is meant past environmental data. This may be gathered in anysuitable time window, and may include data within a window which extendsup to but not including the present time.

At step 210, historical time-dependent operational characteristic dataassociated with the one or more renewable power sources. By historicalis meant past operational characteristic data such as power output. Thismay be gathered in any suitable time window, and may include data withina window which extends up to but not including the present time.

At step 220, a machine learning model is trained based on the historicaltime-dependent environmental data and the historical time-dependentoperational characteristic data captured in steps 200 and 210. The modelmay be trained any suitable number of times and at pre-determinedintervals or on demand.

At step 230, the trained machine learning model is executed to predictavailable power resources for the one or more industrial gas plants fora pre-determined future time period.

At step 240, the predicted data may optionally be used to control theone or more industrial gas plants in response to the predicted availablepower resources for the pre-determined future time period. For example,one or more operational setpoints of the one or more industrial gasplants may be set in dependence upon the predicted available powerresources.

In addition, as described above, an optimization algorithm may beutilized based on the predicted available power resources for apre-determined future period to determine how best to manage storageresource 28 which includes resource storage such as Hydrogen storage 14and Nitrogen storage 16 and energy storage resources 28 a, 28 b, 28 c.The optimization algorithm may select the optimal scheduling and/orselect the optimal resources for a given pattern of predicted poweravailability as a function of time.

The selection and scheduling may be based on operational characteristicsof the storage resources; for example, the fill levels, pressures andother characteristics of the Hydrogen and Nitrogen storage 14, 16 a, orthe ramp up/ramp down, time dependency, maximum generated power/maximumstored energy operational profiles of the energy storage resources 28 a,28 b, 28 c.

At step 250, a determination is made as to whether a further trainingprocess is required. This may be based on an empirical metric such as apre-determined time period. Alternatively, it may be based on anassessment of the accuracy of the machine learning model by comparing avalue of the predicted power resources for a pre-determined future timeperiod with the actual power resources which were available to theindustrial gas plant at the end of the predicted future time period.This enables determination of a prediction error value which provides ametric for the accuracy of the model.

If it is determined that the model needs retraining, a training time maybe scheduled and the method may move back to training in step 220. Notethat this may occur following or during either of steps 230 or 240.

Plant Operation Module (POM) 154

Optimization of the plant process control is critical to achievingefficiency. Given that renewable power sources will nearly always resultin variations in the available power, the industrial gasplant complex 10may frequently be operating in a dynamic mode. This requires real-timeperformance models to devise a robust operational strategy. The POM 154comprises a machine learning and physics-based model to model plantoperation. The physics-based models may be utilised to generatepredictor variables indicative of operational characteristics of therespective industrial gas plant. These may be used, in embodiments,together with other operational plant data (e.g. power input, poweroutput, gas output) as time-dependent data inputs to a respectivemachine learning model.

As noted above, the industrial gasplant complex 10 comprises theHydrogen production plant 12, the Hydrogen storage unit 14, the AirSeparation Unit (ASU) 16, the Ammonia synthesis plant 18 and the ammoniastorage unit 20, some of which are controlled by respective controlsystems: Hydrogen production plant control system 112, Hydrogen storagecontrol system 114, ASU control system 116 and Ammonia synthesis plantcontrol system 118.

At every time step, in embodiments, the power availability forecast fromPPM 152 is used to define the operational setpoints of the differentindustrial gas plants 112, 114, 116, 118.

These decisions are operable in embodiments to achieve a high processefficiency. This requires accurate quantitative understanding of thedifferent process units in terms of their real-time performance, systemavailability information together with resource availability andimpending maintenance issues.

The POM 154 is configured to capture the time-varying attributes foreach of the subsystems (i.e. the respective industrial gas plants) andpredict the production efficiency of the overall process in terms ofAmmonia produced for a given level of energy consumed.

A high-fidelity model for such a prediction is based on real-timemachine learning framework, which uses time-dependent historical dataprovided by aa Distributed Control System (DCS) in the form of timeseries for various process tags. Any suitable machine learning algorithmmay be used to build ensemble models for individual subsystems. Forexample, the model may utilise techniques such as Gradient boosting(utilising, for example, XGboost), Long short-term memory (LSTM),support vector machine (SVM) or random decision forests.

POM 154—Hydrogen Production Plant 12 Modelling

Water electrolysis is an energy intensive process and a key process stepin the production of Green Hydrogen. Each of the electrolyser modules 12a, 12 b . . . 12 n of the Hydrogen production plant 12 is made up ofhundreds of electrolytic cells working together to covert the renewablepower into molecules of hydrogen governed by the time-dependent moduleefficiency η.

Each of the electrolyser modules 12[k] in the Hydrogen production plant12 is modelled independently based on its historical performance data.The predicted operational characteristic variables utilized are:

Electrolyzer Power Consumed [EP(i,k)]

Electrolyzer Hydrogen Produced [EH(i,k)]

Which are based on number of predictor variables such as:

Demin Water Flow [ED(i,k)]

Average cell temperature [ECT(i,k)],

Average cell pressure [ECP(i,k)],

Current flowing through the electrodes [I(i,k)]

Amongst other key process indicators.

Historical time series data for predictor and response variables aresampled over several months sampled at appropriate frequency [s] and areused to develop a model for the actual module efficiency. The model isbuilt using equations derived from the predictor variables.

In addition, the totalizer variable is used to track the functioning ageof the module, which is one of the predictor variables in the model.Reliability and maintenance event information from asset managementsystem.

The model will be trained periodically or on demand (to create arelationship between response variables at time n with respect topredictor variables at each of the time instances starting from n−1 ton-k in intervals of fixed duration such as 15 minutes, or 1 hour.

In the training system various machine learning algorithms are used tocreate a mathematical relationship between the predicted and predictorvariables.

POM 154—Ammonia Loop Modelling

The Ammonia Loop is a single unit equilibrium reactive system whichprocesses the synthesis gas of nitrogen and hydrogen to produce ammonia.Nitrogen is provided by the ASU 16 which, in embodiments, is runningcontinuously to provide Nitrogen.

Hydrogen is provided from the Hydrogen production plant 12 if it isrunning based on the availability of the renewable power at giveninstance or else hydrogen is fed from the Hydrogen storage 14.

Stoichiometric composition of synthesis gas is processed by the syn-gascompressor system and the product is refrigerated by another set ofcompressors and sent to storage.

The performance of the Ammonia loop is governed by the equilibriumconversion of the exothermic reaction and is monitored in real-timebased on the predictive model for Ammonia flow to storage, AFi as afunction various predictor variables, including:

Power consumed by Ammonia loop, APi,

Ammonia loop pressure and Temperature, ALPi, ALTi

Feed flow rates of nitrogen and hydrogen streams, ANFi, AHFi

Ammonia plant syngas compressor pressure, ACPi.

Additional information on the health of the catalyst bed and timing ofmaintenance events may be used in the model to get the most realisticpicture of the conversion loop efficiency.

A further aspect of the Ammonia loop is the different modes ofoperation. In embodiments, two main modes are present: Normal andStand-by. The Normal model involves ramping up and down in response tothe amount of Hydrogen available. This data is tracked in the DCS and isutilized to see any performance differences or to diagnose any processdeviations from production planning. This time-dependent operationalcharacteristic data may be utilized as inputs to the trained machinelearning model to predict future operational behaviour.

As set out below, the model is trained periodically over a longer-rangehistorical data set (which may be, for example, 6 months to a year) tocapture all modes and different levels of ramp rates.

POM 154—Hydrogen Storage 14 Modelling

The Hydrogen storage unit 14 comprises, in embodiments, Hydrogenpurification trains, storage and a set of compressors which are operablein a dynamic fashion to direct the Hydrogen delivery to the Ammoniaprocess plant and manage the Hydrogen inventory to avoid shutdowns dueto lack of available gas resources.

The overall performance of the system is measured by achieving thespecified set points on header pressure in a reliable and energyefficient manner. The compressors are modelled in terms of iso-entropicefficiencies based on operating temperatures and ambient conditions.Real-time condition monitoring of all the compressors is based onadaptive multi-variate (Principal Component Analysis (PCA) and PartialLeast Squares (PLS) models are built on moving 3-month window ofhistorical data of key process tags.

Real-time tracking of Hydrogen storage may be based on first principalthermodynamic models based on using:

Storage system pressure and temperature, SPi, STi

Hydrogen compressor pressure and flow, HCPi, HCFi.

The efficiency of the compressor system is tracked using the compressorpower consumed, CPi. In addition this system may perform the real-timemonitoring of the Hydrogen purification system in terms of bothefficiency and reliability.

POM 154—ASU 16 Modelling

In order to model the Air Separation Unit 16, in embodiments,multi-variate partial least squares (PLS) and principle componentanalysis (PCA) models along with engineering models are utilized. Thesemodels are operable to diagnose performance impacts and and identifypreferred operating modes. Data from these models may be utilized asoperational characteristic data input into a trained machine learningmodel.

Several key performance indicators (KPIs) may be selected. Innon-limiting examples they may comprise specific power, N₂ recovery, andtemperature differences in a heat exchanger forming part of the ASU.)The KPIs are tracked in real-time for early detection and diagnosis ofinefficient operations as well as emerging degradation of equipmenthealth. The generation of the KPIs are not material to the presentinvention however these values may be utilised by the predictive machinelearning model to build a predictive model of the performance of the ASU16.

Industrial Gas Plant Model Component Training and Prediction

For each industrial gas plant, a machine learning model is assigned andimplemented as set out above. For each plant, the machine learning modelis operable to utilise a training process to generate equations whichmodel the behaviour of the respective industrial gas plant. This is doneusing the predictor variables set out above for each industrial gasplant.

The training process may take historical time-dependent operationalcharacteristic data as an input to train the machine learning model. Theoperational characteristic data may comprise physical measured datarelating to the respective industrial gas plant such as, in non-limitingembodiments, input power, power usage, gas output, measured efficiencyetc.

In addition, the operational characteristic data may comprise datagenerated by one or more physics-based models as discussed above. Thephysics-based models may take measured specific industrial gas plantcharacteristics (specific to each industrial gas plant) and may generateone or more metrics indicative of the performance of the industrial gasplant. These time-dependent metrics may then be used as inputtedoperational characteristic data to train the machine learning modelassigned to the respective industrial gas plant.

Once a training process at a training time is complete, the model can beused to predict the behaviour of the respective industrial gas plant. Inthe prediction stage, the equations generated and stored in the trainingphase are used to enable predictions regarding future behaviour to bemade.

In embodiments, an assessment of the accuracy of the machine learningmodel is made by comparing a value of the predicted future behaviour ofeach industrial gas plant for a pre-determined future time period withthe actual behaviour of the industrial gas plant at the end of thepredicted future time period. This enables determination of a predictionerror value which provides a metric for the accuracy of the model.

If the model is insufficiently accurate, it may need to be trained at afurther training time. The pre-determined training time may be selectedbased on the prediction error value which may, in embodiments, be whenthe prediction error value exceeds a pre-determined threshold.

In embodiments, the training time may be selected empirically on aperiodic basis; for example, every 24 hours or every 48 hours asappropriate. Given, in embodiments, that historical data may extend overa number of months, training may not need to be so frequent. Inembodiments, this periodic basis may be the default training timestrategy.

However, in embodiments, this may be interrupted when the predictionerror value exceeds the pre-determined threshold within thepre-determined empirical interval, in which case the training time isscheduled based on the prediction error value.

POM 154 Summary

As discussed above, machine learning models are provided for each of theindustrial gas plants forming the industrial gas complex. These machinelearning models are trained on operational characteristic datagenerated, as described above, from measured historical time-dependentdata relating to the relevant plant and/or from time-dependent datagenerated from one or more physics-based models of the respective plant.

These models each generate predicted data relating to one or more of:the performance; the capability; the efficiency; the maintenance status;and/or the utilization of the relevant plant.

An example of a generated efficiency curve for a process variable isshown in FIG. 7. FIG. 7 shows a curve of efficiency of electrolyser vsload on an electrolyser. This curve is generated from time-dependentoperational characteristic data from an electrolyser forming part of theHydrogen plant 12 which is input into the respective machine learningmodel to generate operational characteristic data.

In addition to the predictions of the individual machine learning modelsassigned to each industrial gas plant, a further model may be utilizedwhich determines the overall performance of the plant complex at a giveninstance is based on combining the efficiency of individual modulescoming from each of the models above. Ensemble machine learningalgorithms are employed to improve the quality of predictions and bestmodels are selected to be used in the prediction system.

In other words, the time-varying operational characteristics for each ofthe industrial gas plants forming part of the plant complex for apre-determined future period are predicted by each of the trainedmachine learning models and input into a further model to predict theproduction efficiency of the overall process plant complex. Data fromeach of the models may be input into the collective model on a periodicbasis; for example, in non-limiting embodiments this may be every 15minutes.

In terms of the Ammonia production plant in the exemplary embodiment,the efficiency determination enables a predicted determination of theAmmonia produced for a given level of energy input.

All the modelling carried out in the POM 154 is implemented as computerprograms on one or more computers in the plant. Irrespective of themachine learning algorithm used, the model utilises a two step operationprocess where a training stage is required prior to a predictive stage.Both stages are implemented as computer programs on one or more computersystems.

Specialist and non-specialist hardware may also be used. For example,the training stage may involve use of Central Processing unit (CPU) andGraphical Processing Unit (GPU) components of a computer system. Inaddition, other specialist hardware may be used such as FieldProgrammable Gate Arrays (FPGAs), Application Specific IntegratedCircuits (ASICs) or other stream processor technologies.

The model execution computer(s) will be connected to other computerdatabase systems where the data from the plant and weather data servicewill be stored. As the plant is operating, performance models are usedto make predictions every 15 minutes to get the production profiles.

All the performance models along with some of the unit-operation leveldata is used in the RTOM 156 at a pre-defined frequency to optimize theoperational efficiency and define the setpoints for different models.

Method of Operation

FIG. 6 shows a method according to an embodiment. Note the followingsteps need not be carried out in the order described below and somesteps may be carried out concurrently with other steps.

In embodiments, a method of predicting operational characteristics of anindustrial gas plant complex comprising a plurality of industrial gasplants is provided. The method is executed by at least one hardwareprocessor.

At step 300, a machine learning model is assigned to each of theindustrial gas plants forming the industrial gas plant complex. Thismodel may take any suitable form as described above. It may utilizehistorical time-dependent operational characteristics of the industrialgas plant to generate equations to make future predictions.

At step 310, the respective machine learning model for each industrialgas plant is trained based on received historical time-dependentoperational characteristic data for the respective industrial gas plant.This data may take any suitable form and may be specific to a particulartype of industrial gas plant as described above. By historical is meantpast operational characteristic data. This may be gathered in anysuitable time window, and may include data within a moving window whichextends up to but not including the present time. The window may inembodiments be six months to a year long.

The historical time-dependent operational characteristic data maycomprise physical measured data relating to the respective industrialgas plant such as, in non-limiting embodiments, input power, powerusage, gas output, measured efficiency etc.

In addition, the operational characteristic data may comprise datagenerated by one or more physics-based models as discussed above. Thephysics-based models may take measured specific industrial gas plantcharacteristics (specific to each industrial gas plant) and may generateone or more metrics indicative of the performance of the industrial gasplant. These time-dependent metrics may then be used as inputtedoperational characteristic data to train the machine learning modelassigned to the respective industrial gas plant.

At step 320, the trained machine learning model for each industrial gasplant is executed to predict operational characteristics for eachrespective industrial gas plant for a pre-determined future time period.This prediction may optionally be used to control the behavior of therespective industrial gas plant, to predict likely usage, maintenanceschedules, resource allocation or to identify process issues andpotential problems.

For example, the predicted data may be used to infer other technicalproperties of the industrial gas plant(s). The predictions may be usedto determine resource planning, maintenance schedules or servicingrequirements. This scheduling of maintenance may be done in conjunctionwith determination of power resources and capacity of storage units. Forexample, the maintenance of a gas-generating component (e.g.electrolyzers, ASUs, Ammonia production plant) may be scheduled to occurduring a period when gas stores are high and predicted availablerenewable power is low so as to minimize disruption and maintaincontinuity of service provision.

In addition, the data may also be utilized to determine setpointcharacteristics in step 330 below.

At step 330, the predicted operational characteristics determined by themodels in step 320 for each respective industrial gas plant are utilizedin a further collective model to generate an operational performancemetric of the industrial gas plant complex. In the Ammonia productionplant in the exemplary embodiment, the efficiency determination enablesa predicted determination of the Ammonia produced for a given level ofenergy input.

At step 340, the predicted data for a pre-determined future time periodis compared to actual measured data at the end of the time period. Thiscomparison is used to infer other technical properties of the industrialgas plant(s). The predictions may be used to determine resourceplanning, maintenance schedules or servicing requirements.

In embodiments, a prediction for a pre-determined time period (e.g. atime window of, for example, 2 weeks, one month, six months fromgeneration of the predicted data or from a time stamp in the predicteddata) is then compared to actual data for the time window or time periodat the end of the time period covered by the predicted data (e.g. after2 weeks/one month/six months from the predicted data generation or fromthe time stamp in the predicted data). This enables potential problemsin the plant complex 10 to be identified early since deviation of anyindustrial gas plant or storage system from a predicted model based onpast actual behaviour may indicate development of a production ormaintenance problem.

By using such a method, potential future problems can be identifiedearly, enabling remedial action to be taken before any critical failuresor unplanned shut-downs of the plant services are required for urgentmaintenance or repair.

In addition, in embodiments, the scheduling of maintenance may be donein conjunction with determination of power resources and capacity ofstorage units. For example, the maintenance of a gas-generatingcomponent (e.g. electrolyzers, ASUs, Ammonia production plant) may bescheduled to occur during a period when gas stores are high andpredicted available renewable power is low so as to minimize disruptionand maintain continuity of service provision.

In embodiments, the predicted data may optionally be used to control theone or more industrial gas plants in response to the predictedoperational characteristics of the industrial gas plants for thepre-determined future time period. For example, one or more operationalsetpoints of the one or more industrial gas plants may be set independence upon the predicted behavior.

The control in step 340 may be done in conjunction with the methoddescribed in steps 200 to 250 where predicted power availability isutilized in combination with the predicted efficiency determination instep 330 to enable set points to be determined based on both predictedpower availability and also plant efficiency.

At step 350, a determination is made as to whether a further trainingprocess is required. This may be based on an empirical metric such as apre-determined time period. Alternatively, it may be based on anassessment of the accuracy of the machine learning model by comparing avalue of the predicted operational characteristics for a pre-determinedfuture time period with the actual operational characteristics of theindustrial gas plant at the end of the predicted future time period.This enables determination of a prediction error value which provides ametric for the accuracy of the model.

If it is determined that the model needs retraining, a training time maybe scheduled and the method may move back to training in step 310. Notethat this may occur following any of steps 320, 330 or 340.

Real-Time Optimization Module (RTOM) 156

The RTOM 156 comprises a system to determine the rate at which variousindustrial gas plants should run to manage the renewable hydrogenproduction and storage optimally while maximizing ammonia production. Inother words, the RTOM 156 enables real-time optimization of anindustrial gas plant complex using renewable power. More particularly,in embodiments,

In embodiments, the system solves an optimization algorithm applied to adynamic mathematical model of an industrial gas plant complex. The RTOM156 system uses the predicted powers WPi and SPi and state of theindustrial gas plants such as efficiencies inferred from plant-specificfactors discussed above in relation to the POM 154 such as current,pressure, temperature and flowrate measurements. These values are takenas the inputs and applies an optimization algorithm to propose optimalrates at which to run the ammonia plant for time periods from c+1 toc+p.

The rates of the other industrial gas plants such as Hydrogen productionplant 12, hydrogen compression and storage system 14, the air separationunit 16, Nitrogen storage 16 a and the water plant are linked to ammoniarate and are controlled by lower-level controllers as described above.Only the first value from the list of optimal value for time c+1 isimplemented, and the calculation is repeated at time c+1 with new dataas it becomes available.

The RTOM 156 may also utilise data relating to the resource storagedevices or energy storage devices of the storage resource 28. As notedabove, the energy storage devices may comprise one or more of: a BatteryEnergy Storage System (BESS) 28 a, a Compressed/Liquid Air EnergySystems (CAES or LAES) 28 b or a Pumped Hydro Storage System (PHSS) 28c. The status, operational characteristics, availability, resourcestorage level and ease of power availability of each of the units of thestorage resource 28 may be factored in to the optimization problem.

The RTOM 156 system is implemented on a computer and receives variousinputs from other computer systems. The predicted power comes from PPM152. The state of the industrial gas plants are represented by equationsrelating power consumptions of various units such as electrolyzer (ECi),hydrogen compressor (CPi), ammonia plant (APi), and ASU (NPi) to processvariables related to these units.

The RTOM 156 system solves an optimization problem which seeks tomaximize ammonia production given constraints on total amount ofpredicted power available over the next p time periods, amount ofhydrogen available in storage, physics or data-based equationsdescribing process plant operation.

Typically such problems are Mixed Integer Non Linear Programs (MINLP)because the process plant equations are non-linear and some of thedecisions require some equipment to be run in one of a few possiblemodes leading to integer variables. The optimization generates setpoints from time c+1 to c+p to balance the predicted generated power andconsumed power so that right amount of hydrogen is produced and ammoniaplant runs at the correct rate to maximize ammonia production. The RTOM156 system also accounts for hydrogen storage and hydrogen may either bestored or consumed from storage based on future power prediction.

In addition, the RTOM 156 system can choose to recommend that the plantmay go into standby or shutdown modes based on power availability andequipment availability information. The output of the RTOM 156 system isa recommend ammonia production rate that is transferred automatically toadvanced control systems controlling the ammonia plant, ASU,electrolyzer, water plant and hydrogen compression and storage.

An example of this is shown in FIG. 8 which shows the optimal Ammoniaplant rate as a function of time for a 48 hour predicted period.

Method of Operation

In embodiments, there is provided a method of controlling an industrialgas plant complex comprising a plurality of industrial gas plantspowered by one or more renewable power sources. The method is executedby at least one hardware processor.

At step 410, time-dependent predicted power data for a pre-determinedfuture time period from the one or more renewable power sources isreceived. In non-limiting embodiments, this may be determined by the PPM152 in accordance with the process and method discussed at steps 200 to250. Alternatively, the predicted power data may be obtained inaccordance with any other suitable process.

At step 420, time-dependent predicted operational characteristic datafor each industrial gas plant is received. In this context “industrialgas plant” may also include industrial gas storage, for example,Hydrogen, Nitrogen and/or Ammonia storage as described above in thepresent embodiments.

The operational characteristic data may, in embodiments, be generated inaccordance with the protocols described in relation to the POM 156 andmethod steps 300 to 350 of FIG. 6. However, said data may also bedetermined in accordance with any other suitable process.

At step 430, the predicted power data and predicted characteristic datais utilized in an optimization model to generate a set of statevariables (which may be optimized state variables) for the plurality ofindustrial gas plants. In embodiments, this may be done by solving anoptimization problem. For example, in non-limiting embodiments theoptimization model may define the predicted power data and predictedcharacteristic data as a set of non-linear equations. Storage resourcedata may optionally be included in the power data. The state variablesare then generated by solving the set of non-linear equations.

At step 440, the generated state variables (which may be optimized statevariables) are utilized to generate a set of control set points for theplurality of industrial gas plants. The set points may be defined toachieve any specified goal. For example, the set points may be definedat a particular time to ensure that the industrial gas plant(s) areoperated efficiently and effectively given the power and storageresources available. The set points may alternatively or additionally beutilized to maximize the production of industrial gas given thepredicted power availability.

As a further example, the predicted power availability and predictedefficiency and operational characteristics of the individual industrialgas plants and/or the industrial gas plant complex as a whole may beutilized to prevent power starvation of the individual plants and/orplant complex or to increase production in situations where poweravailability is plentiful at the present time but a future shortfall ispredicted.

At step 450, the control set points are sent to a control system tocontrol the industrial gas plant complex by adjusting one or morecontrol set points of the industrial gas plants.

In summary, in exemplary embodiments a control and optimization systemfor a industrial gas plant complex is provided. The industrial gas plantcomplex comprises a plurality of industrial gas plants, including aHydrogen production plant utilizing electrolysis of water, an airseparation plant for production of nitrogen, an ammonia synthesis plantto produce ammonia, a hydrogen storage system and an ammonia storage andshipment system.

In exemplary embodiments, the system seeks to maximize ammoniaproduction given an uncertain future power input for the system. Thesystem also includes a machine learning based program to predict futurepower input based on weather forecast. This program continuously learnsfrom environmental and weather patterns and power generated to makefuture power generation predictions. The system also includes anothermachine learning program to learn from industrial gas plants operatingdata and modify the mathematical model used for optimization.

While the invention has been described with reference to the preferredembodiments depicted in the figures, it will be appreciated that variousmodifications are possible within the spirit or scope of the inventionas defined in the following claims.

In the specification and claims, the term “industrial gas plant” isintended to refer to process plants which produce, or are involved inthe production of industrial gases, commercial gases, medical gases,inorganic gases, organic gases, fuel gases and green fuel gases eitherin gaseous, liquified or compressed form.

For example, the term “industrial gas plant” may include process plantsfor the manufacture of gases such as those described in NACE class 20.11and which includes, non-exhaustively: elemental gases; liquid orcompressed air; refrigerant gases; mixed industrial gases; inert gasessuch as carbon dioxide; and isolating gases. Further, the term“industrial gas plant” may also include process plants for themanufacture of industrial gases in NACE class 20.15 such as Ammonia,process plants for the extraction and/or manufacture of methane, ethane,butane or propane (NACE classes 06.20 and 19.20), and manufacture ofgaseous fuels as defined by NACE class 35.21. The above has beendescribed with respect to the European NACE system but is intended tocover equivalent classes under the North American classifications SICand NAICS. In addition, the above list is non-limiting andnon-exhaustive.

In some of the examples a hydrogen storage system and in some cases apurification unit are shown. However, it will be appreciated that thepresent invention can be implemented without the use of a hydrogenstorage system or purification unit, which are only shown here forcompleteness.

In this specification, unless expressly otherwise indicated, the word“or” is used in the sense of an operator that returns a true value wheneither or both of the stated conditions are met, as opposed to theoperator “exclusive or” which requires only that one of the conditionsis met. The word “comprising” is used in the sense of “including” ratherthan to mean “consisting of”.

In the discussion of embodiments of the present invention, the pressuresgiven are absolute pressures unless otherwise stated.

All prior teachings above are hereby incorporated herein by reference.No acknowledgement of any prior published document herein should betaken to be an admission or representation that the teaching thereof wascommon general knowledge in Australia or elsewhere at the date thereof.

Where applicable, various embodiments provided by the present disclosuremay be implemented using hardware, software, or combinations of hardwareand software. Also, where applicable, the various hardware componentsand/or software components set forth herein may be combined intocomposite components comprising software, hardware, and/or both withoutdeparting from the spirit of the present disclosure. Where applicable,the various hardware components and/or software components set forthherein may be separated into sub-components comprising software,hardware, or both without departing from the scope of the presentdisclosure. In addition, where applicable, it is contemplated thatsoftware components may be implemented as hardware components andvice-versa.

Software, in accordance with the present disclosure, such as programcode and/or data, may be stored on one or more computer readablemediums. It is also contemplated that software identified herein may beimplemented using one or more general purpose or specific purposecomputers and/or computer systems, networked and/or otherwise. Whereapplicable, the ordering of various steps described herein may bechanged, combined into composite steps, and/or separated into sub-stepsto provide features described herein.

While various operations have been described herein in terms of“modules”, “units” or “components,” it is noted that that terms are notlimited to single units or functions. Moreover, functionality attributedto some of the modules or components described herein may be combinedand attributed to fewer modules or components. Further still, while thepresent invention has been described with reference to specificexamples, those examples are intended to be illustrative only, and arenot intended to limit the invention. It will be apparent to those ofordinary skill in the art that changes, additions or deletions may bemade to the disclosed embodiments without departing from the spirit andscope of the invention. For example, one or more portions of methodsdescribed above may be performed in a different order (or concurrently)and still achieve desirable results.

What is claimed is:
 1. A method of controlling an industrial gas plantcomplex comprising a plurality of industrial gas plants powered by oneor more renewable power sources, the method being executed by at leastone hardware processor, the method comprising: receiving time-dependentpredicted power data for a pre-determined future time period from theone or more renewable power sources; receiving time-dependent predictedoperational characteristic data for each industrial gas plant; utilizingthe predicted power data and predicted characteristic data in anoptimization model to generate a set of state variables for theplurality of industrial gas plants; utilizing the generated statevariables to generate a set of control set points for the plurality ofindustrial gas plants; and sending the control set points to a controlsystem to control the industrial gas plant complex by adjusting one ormore control set points of the industrial gas plants.
 2. The method ofclaim 1, wherein the optimization model defines the predicted power dataand predicted characteristic data as a set of non-linear equations. 3.The method of claim 2, wherein the state variables are generated bysolving the set of non-linear equations.
 4. The method of claim 1,wherein the time-dependent predicted power data is generated from atrained machine learning model.
 5. The method of claim 4, wherein thetime-dependent predicted power data is obtained by: obtaining historicaltime-dependent environmental data associated with the one or morerenewable power sources; obtaining historical time-dependent operationalcharacteristic data associated with the one or more renewable powersources; training a machine learning model based on the historicaltime-dependent environmental data and the historical time-dependentoperational characteristic data; and executing the trained machinelearning model to predict available power resources for the one or moreindustrial gas plants for a pre-determined future time period.
 6. Themethod of claim 1, wherein the time-dependent predicted operationalcharacteristic data is generated from a trained machine learning modelfor each of the industrial gas plants.
 7. The method of claim 6, whereinthe time-dependent predicted operational characteristic data for eachindustrial plant is obtained by: assigning a machine learning model toeach of the industrial gas plants forming the industrial gas plantcomplex; training the respective machine learning model for eachindustrial gas plant based on received historical time-dependentoperational characteristic data for the respective industrial gas plant;and executing the trained machine learning model for each industrial gasplant to predict operational characteristics for each respectiveindustrial gas plant for a pre-determined future time period.
 8. Themethod of claim 1, wherein the industrial gas plant complex comprisesstorage resources comprising one or more industrial gas storage vesselsand/or one or more energy storage resources.
 9. The method of claim 8,wherein the one or more energy storage resources comprises one or moreof: battery energy storage systems; compressed air energy storage;liquid air energy storage; or pumped hydroelectric energy storage. 10.The method of claim 8, wherein the predicted power data furthercomprises data representative of operational parameters of the storageresources.
 11. The method of claim 10, wherein the data representativeof operational parameters of the storage resources comprises one or moreof: resource storage availability; fill level; and utilization.
 12. Asystem for controlling an industrial gas plant complex comprising aplurality of industrial gas plants powered by one or more renewablepower sources, the system comprising: at least one hardware processoroperable to perform: receiving time-dependent predicted power data for apre-determined future time period from the one or more renewable powersources; receiving time-dependent predicted operational characteristicdata for each industrial gas plant; utilizing the predicted power dataand predicted characteristic data in an optimization model to generate aset of state variables for the plurality of industrial gas plants;utilizing the generated state variables to generate a set of control setpoints for the plurality of industrial gas plants; and sending thecontrol set points to a control system to control the industrial gasplant complex by adjusting one or more control set points of theindustrial gas plants.
 13. The system of claim 12, wherein theoptimization model defines the predicted power data and predictedcharacteristic data as a set of non-linear equations.
 14. The system ofclaim 13, wherein the state variables are generated by solving the setof non-linear equations.
 15. The system of claim 12, wherein thetime-dependent predicted power data is generated from a trained machinelearning model.
 16. The system of claim 12, wherein the time-dependentpredicted operational characteristic data is generated from a trainedmachine learning model for each of the industrial gas plants.
 17. Acomputer readable storage medium storing a program of instructionsexecutable by a machine to perform a of controlling an industrial gasplant complex comprising a plurality of industrial gas plants powered byone or more renewable power sources, the method being executed by atleast one hardware processor, the method comprising: receivingtime-dependent predicted power data for a pre-determined future timeperiod from the one or more renewable power sources; receivingtime-dependent predicted operational characteristic data for eachindustrial gas plant; utilizing the predicted power data and predictedcharacteristic data in an optimization model to generate a set of statevariables for the plurality of industrial gas plants; utilizing thegenerated state variables to generate a set of control set points forthe plurality of industrial gas plants; and sending the control setpoints to a control system to control the industrial gas plant complexby adjusting one or more control set points of the industrial gasplants.
 18. The computer readable storage medium of claim 17, whereinthe optimization model defines the predicted power data and predictedcharacteristic data as a set of non-linear equations.
 19. The computerreadable storage medium of claim 18, wherein the state variables aregenerated by solving the set of non-linear equations.
 20. The computerreadable storage medium of claim 17, wherein the time-dependentpredicted power data is generated from a trained machine learning modeland/or the time-dependent predicted operational characteristic data isgenerated from a trained machine learning model for each of theindustrial gas plants.